{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Randomized Benchmarking\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "**Randomization benchmarking (RB)** is a well-known technique to measure average gate performance by running sequences of random Clifford gates that should return the qubits to the initial state. \n",
    "Qiskit Ignis has tools to generate one- and two-qubit Clifford gate sequences simultaneously. \n",
    "\n",
    "This notebook gives an example for how to use the ``ignis.verification.randomized_benchmarking`` module. This particular example shows how to run 2-qubit randomized benchmarking (RB) simultaneous with 1-qubit RB. There are also examples on how to use some of the companion functions for predicting RB fidelity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:09.958187Z",
     "start_time": "2019-12-10T21:49:08.131232Z"
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   "source": [
    "#Import general libraries (needed for functions)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "#Import Qiskit classes\n",
    "import qiskit\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error\n",
    "\n",
    "#Import the RB Functions\n",
    "import qiskit.ignis.verification.randomized_benchmarking as rb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Select the Parameters of the RB Run <a name='select_params_RB'></a>\n",
    "\n",
    "First, we need to choose the following parameters:\n",
    "\n",
    "- **nseeds:** The number of seeds. For each seed you will get a separate list of output circuits in rb_circs.\n",
    "- **length_vector:** The length vector of Clifford lengths. Must be in ascending order. RB sequences of increasing length grow on top of the previous sequences.\n",
    "- **rb_pattern:** A list of the form [[i,j],[k],...] which will make simultaneous RB sequences where Qi,Qj are a 2-qubit RB sequence and Qk is a 1-qubit sequence, etc. The number of qubits is the sum of the entries. For 'regular' RB the qubit_pattern is just [[0]],[[0,1]].\n",
    "- **length_multiplier:** If this is an array it scales each rb_sequence by the multiplier.\n",
    "- **seed_offset:** What to start the seeds at (e.g. if we want to add more seeds later).\n",
    "- **align_cliffs:**  If true adds a barrier across all qubits in rb_pattern after each set of cliffords.\n",
    "\n",
    "In this example we have 3 qubits Q0,Q1,Q2. \n",
    "We are running 2Q RB (on qubits Q0,Q2) and 1Q RB (on qubit Q1) simultaneously, \n",
    "where there are twice as many 1Q Clifford gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "end_time": "2019-12-10T21:49:09.963476Z",
     "start_time": "2019-12-10T21:49:09.960218Z"
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   "source": [
    "#Number of qubits\n",
    "nQ = 3\n",
    "#There are 3 qubits: Q0,Q1,Q2.\n",
    "#Number of seeds (random sequences)\n",
    "nseeds = 5\n",
    "#Number of Cliffords in the sequence (start, stop, steps)\n",
    "nCliffs = np.arange(1,200,20)\n",
    "#2Q RB on Q0,Q2 and 1Q RB on Q1\n",
    "rb_pattern = [[0,2],[1]]\n",
    "#Do three times as many 1Q Cliffords\n",
    "length_multiplier = [1,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Generate the RB sequences <a name='gen_RB_seq'></a>\n",
    "\n",
    "We generate RB sequences. We start with a small example (so it doesn't take too long to run).\n",
    "\n",
    "In order to generate the RB sequences **rb_circs**, which is a list of lists of quantum circuits, \n",
    "we run the function `rb.randomized_benchmarking_seq`.\n",
    "\n",
    "This function returns:\n",
    "\n",
    "- **rb_circs:** A list of lists of circuits for the rb sequences (separate list for each seed).\n",
    "- **xdata:** The Clifford lengths (with multiplier if applicable)."
   ]
  },
  {
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   "execution_count": 3,
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     "end_time": "2019-12-10T21:49:15.001889Z",
     "start_time": "2019-12-10T21:49:09.966440Z"
    },
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   "outputs": [],
   "source": [
    "rb_opts = {}\n",
    "rb_opts['length_vector'] = nCliffs\n",
    "rb_opts['nseeds'] = nseeds\n",
    "rb_opts['rb_pattern'] = rb_pattern\n",
    "rb_opts['length_multiplier'] = length_multiplier\n",
    "rb_circs, xdata = rb.randomized_benchmarking_seq(**rb_opts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we print the circuit corresponding to the first RB sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:15.015115Z",
     "start_time": "2019-12-10T21:49:15.004449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        ┌───┐┌───┐┌───┐      ░  ┌───┐ ┌─────┐┌───┐            »\n",
      "qr_0: |0>────────────■──┤ H ├┤ S ├┤ X ├──────░──┤ X ├─┤ Sdg ├┤ H ├────────────»\n",
      "         ┌───┐┌───┐  │  ├───┤└─░─┘├───┤┌───┐ ░  ├───┤ └──░──┘├───┤┌─────┐┌───┐»\n",
      "qr_1: |0>┤ H ├┤ H ├──┼──┤ S ├──░──┤ H ├┤ S ├────┤ X ├────░───┤ H ├┤ Sdg ├┤ H ├»\n",
      "         ├───┤├───┤┌─┴─┐├───┤┌───┐└───┘└───┘ ░ ┌┴───┴┐ ┌───┐ └───┘└─────┘└───┘»\n",
      "qr_2: |0>┤ H ├┤ S ├┤ X ├┤ H ├┤ S ├───────────░─┤ Sdg ├─┤ H ├──────────────────»\n",
      "         └───┘└───┘└───┘└───┘└───┘           ░ └─────┘ └───┘                  »\n",
      " cr_0: 0 ═════════════════════════════════════════════════════════════════════»\n",
      "                                                                              »\n",
      " cr_1: 0 ═════════════════════════════════════════════════════════════════════»\n",
      "                                                                              »\n",
      " cr_2: 0 ═════════════════════════════════════════════════════════════════════»\n",
      "                                                                              »\n",
      "«                  ┌─┐           \n",
      "«qr_0: ──■─────────┤M├───────────\n",
      "«        │     ░   └╥┘     ┌─┐   \n",
      "«qr_1: ──┼─────░────╫──────┤M├───\n",
      "«      ┌─┴─┐┌─────┐ ║ ┌───┐└╥┘┌─┐\n",
      "«qr_2: ┤ X ├┤ Sdg ├─╫─┤ H ├─╫─┤M├\n",
      "«      └───┘└─────┘ ║ └───┘ ║ └╥┘\n",
      "«cr_0: ═════════════╩═══════╬══╬═\n",
      "«                           ║  ║ \n",
      "«cr_1: ═════════════════════╬══╩═\n",
      "«                           ║    \n",
      "«cr_2: ═════════════════════╩════\n",
      "«                                \n"
     ]
    }
   ],
   "source": [
    "print(rb_circs[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at the Unitary for 1 Circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Unitary representing each RB circuit should be the identity (with a global phase),\n",
    "since we multiply random Clifford elements, including a computed reversal gate. We simulate this using an Aer unitary simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:15.069700Z",
     "start_time": "2019-12-10T21:49:15.016779Z"
    }
   },
   "outputs": [],
   "source": [
    "#Create a new circuit without the measurement\n",
    "qc = qiskit.QuantumCircuit(*rb_circs[0][-1].qregs,*rb_circs[0][-1].cregs)\n",
    "for i in rb_circs[0][-1][0:-nQ]:\n",
    "    qc.data.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:16.804209Z",
     "start_time": "2019-12-10T21:49:15.071173Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.+1.j -0.+0.j  0.-0.j  0.-0.j -0.-0.j  0.+0.j -0.+0.j  0.-0.j]\n",
      " [-0.-0.j  0.+1.j -0.+0.j  0.-0.j  0.-0.j  0.+0.j -0.-0.j -0.+0.j]\n",
      " [ 0.+0.j -0.+0.j  0.+1.j -0.+0.j -0.-0.j  0.+0.j -0.-0.j  0.+0.j]\n",
      " [-0.+0.j  0.+0.j -0.-0.j  0.+1.j  0.-0.j -0.-0.j  0.-0.j  0.+0.j]\n",
      " [ 0.+0.j  0.-0.j  0.+0.j -0.-0.j  0.+1.j -0.+0.j  0.-0.j -0.+0.j]\n",
      " [ 0.-0.j -0.+0.j -0.+0.j -0.+0.j  0.+0.j  0.+1.j  0.-0.j  0.-0.j]\n",
      " [ 0.+0.j  0.-0.j -0.+0.j  0.-0.j  0.+0.j  0.-0.j  0.+1.j  0.-0.j]\n",
      " [-0.-0.j  0.+0.j  0.-0.j -0.+0.j  0.+0.j  0.+0.j  0.+0.j  0.+1.j]]\n"
     ]
    }
   ],
   "source": [
    "#The Unitary is an identity (with a global phase)\n",
    "backend = qiskit.Aer.get_backend('unitary_simulator')\n",
    "basis_gates = ['u1', 'u2', 'u3', 'cx'] # use U,CX for now\n",
    "job = qiskit.execute(qc, backend=backend, basis_gates=basis_gates)\n",
    "print(np.around(job.result().get_unitary(), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the noise model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a noise model for the simulator. To simulate decay, we add depolarizing error probabilities to the CNOT and U gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:16.809930Z",
     "start_time": "2019-12-10T21:49:16.806016Z"
    }
   },
   "outputs": [],
   "source": [
    "noise_model = NoiseModel()\n",
    "p1Q = 0.002\n",
    "p2Q = 0.01\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p1Q, 1), 'u2')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(2*p1Q, 1), 'u3')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p2Q, 2), 'cx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Execute the RB sequences on Aer simulator <a name='ex_RB_seq'></a>\n",
    "\n",
    "We can execute the RB sequences either using a Qiskit Aer Simulator (with some noise model) or using an IBMQ provider, \n",
    "and obtain a list of results, `result_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating\n"
     ]
    }
   ],
   "source": [
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 200\n",
    "result_list = []\n",
    "transpile_list = []\n",
    "import time\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_circs):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    rb_circ_transpile = qiskit.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = qiskit.execute(rb_circ_transpile, noise_model=noise_model, shots=shots, backend=backend, backend_options={'max_parallel_experiments': 0})\n",
    "    result_list.append(job.result())\n",
    "    transpile_list.append(rb_circ_transpile)    \n",
    "print(\"Finished Simulating\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Fit the RB results and calculate the gate fidelity <a name='fit_RB'></a>\n",
    "\n",
    "### Get statistics about the survival probabilities\n",
    "\n",
    "The results in **result_list** should fit to an exponentially decaying function $A \\cdot \\alpha ^ m + B$, where $m$ is the Clifford length.\n",
    "\n",
    "From $\\alpha$ we can calculate the **Error per Clifford (EPC)**:\n",
    "$$ EPC = \\frac{2^n-1}{2^n} (1-\\alpha)$$\n",
    "(where $n=nQ$ is the number of qubits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:51.169855Z",
     "start_time": "2019-12-10T21:49:51.146737Z"
    }
   },
   "outputs": [],
   "source": [
    "#Create an RBFitter object with 1 seed of data\n",
    "rbfit = rb.fitters.RBFitter(result_list[0], xdata, rb_opts['rb_pattern'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot After 1 Seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:51.597937Z",
     "start_time": "2019-12-10T21:49:51.171988Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "for i in range(2):\n",
    "    ax = plt.subplot(1, 2, i+1)\n",
    "    pattern_ind = i\n",
    "\n",
    "    # Plot the essence by calling plot_rb_data\n",
    "    rbfit.plot_rb_data(pattern_ind, ax=ax, add_label=True, show_plt=False)\n",
    "\n",
    "    # Add title and label\n",
    "    ax.set_title('%d Qubit RB'%(len(rb_opts['rb_pattern'][i])), fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot with the Rest of the Seeds \n",
    "The plot is being updated after each seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:58.814328Z",
     "start_time": "2019-12-10T21:49:51.600375Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rbfit = rb.fitters.RBFitter(result_list[0], xdata, rb_opts['rb_pattern'])\n",
    "\n",
    "for seed_num, data in enumerate(result_list):#range(1,len(result_list)):  \n",
    "    plt.figure(figsize=(15, 6))\n",
    "    axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
    "    \n",
    "    # Add another seed to the data\n",
    "    rbfit.add_data([data])\n",
    "    \n",
    "    for i in range(2):\n",
    "        pattern_ind = i\n",
    "\n",
    "        # Plot the essence by calling plot_rb_data\n",
    "        rbfit.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)\n",
    "\n",
    "        # Add title and label\n",
    "        axis[i].set_title('%d Qubit RB - after seed %d'%(len(rb_opts['rb_pattern'][i]), seed_num), fontsize=18)\n",
    "        \n",
    "    # Display\n",
    "    display.display(plt.gcf())\n",
    "    \n",
    "    # Clear display after each seed and close\n",
    "    display.clear_output(wait=True)\n",
    "    time.sleep(1.0)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add more shots to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:46.051635Z",
     "start_time": "2019-12-10T21:49:58.816353Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating\n"
     ]
    }
   ],
   "source": [
    "shots = 200\n",
    "result_list = []\n",
    "transpile_list = []\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_circs):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    rb_circ_transpile = qiskit.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = qiskit.execute(rb_circ_transpile, noise_model=noise_model, shots=shots, backend=backend, backend_options={'max_parallel_experiments': 0})\n",
    "    result_list.append(job.result())\n",
    "    transpile_list.append(rb_circ_transpile)    \n",
    "print(\"Finished Simulating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:46.784384Z",
     "start_time": "2019-12-10T21:50:46.054323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add this data to the previous fit\n",
    "rbfit.add_data(result_list)\n",
    "\n",
    "#Replot\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "for i in range(2):\n",
    "    ax = plt.subplot(1, 2, i+1)\n",
    "    pattern_ind = i\n",
    "\n",
    "    # Plot the essence by calling plot_rb_data\n",
    "    rbfit.plot_rb_data(pattern_ind, ax=ax, add_label=True, show_plt=False)\n",
    "\n",
    "    # Add title and label\n",
    "    ax.set_title('%d Qubit RB'%(len(rb_opts['rb_pattern'][i])), fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicted Gate Fidelity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the known depolarizing errors on the simulation we can predict the **fidelity**. \n",
    "First we need to count the number of **gates per Clifford**.\n",
    "\n",
    "The function **gates_per_clifford** takes a list of transpiled RB circuits and outputs the number of basis gates in each circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:46.928133Z",
     "start_time": "2019-12-10T21:50:46.789697Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of u1 gates per Clifford: 0.271522 \n",
      "Number of u2 gates per Clifford: 0.923587 \n",
      "Number of u3 gates per Clifford: 0.490109 \n",
      "Number of cx gates per Clifford: 1.526739 \n"
     ]
    }
   ],
   "source": [
    "#Count the number of single and 2Q gates in the 2Q Cliffords\n",
    "gates_per_cliff = rb.rb_utils.gates_per_clifford(transpile_list,xdata[0],basis_gates,rb_opts['rb_pattern'][0])\n",
    "for basis_gate in basis_gates:\n",
    "    print(\"Number of %s gates per Clifford: %f \"%(basis_gate ,\n",
    "                                                  np.mean([gates_per_cliff[0][basis_gate],\n",
    "                                                           gates_per_cliff[2][basis_gate]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function **calculate_2q_epc** gives measured errors in the basis gates that were used to construct the Clifford. \n",
    "It assumes that the error in the underlying gates is depolarizing. It outputs the error per a 2-qubit Clifford.\n",
    "\n",
    "The input to this function is:\n",
    "- **gate_per_cliff:** dictionary of gate per Clifford. \n",
    "- **epg_2q:** EPG estimated by error model.\n",
    "- **qubit_pair:** index of two qubits to calculate EPC. \n",
    "- **list_epgs_1q:** list of single qubit EPGs of qubit listed in ``qubit_pair``.\n",
    "- **two_qubit_name:** name of two qubit gate in ``basis gates`` (default is ``cx``)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted 2Q Error per Clifford: 1.591172e-02\n"
     ]
    }
   ],
   "source": [
    "# Error per gate from noise model\n",
    "epgs_1q = {'u1': 0, 'u2': p1Q/2, 'u3': 2*p1Q/2}\n",
    "epg_2q = p2Q*3/4\n",
    "pred_epc = rb.rb_utils.calculate_2q_epc(\n",
    "    gate_per_cliff=gates_per_cliff,\n",
    "    epg_2q=epg_2q,\n",
    "    qubit_pair=[0, 2],\n",
    "    list_epgs_1q=[epgs_1q, epgs_1q])\n",
    "\n",
    "# Calculate the predicted epc\n",
    "print(\"Predicted 2Q Error per Clifford: %e\"%pred_epc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run an RB Sequence with T1,T2 Errors\n",
    "\n",
    "We now choose RB sequences that contain only 2-qubit Cliffords.\n",
    "\n",
    "We execute these sequences as before, but with a noise model extended with T1/T2 thermal relaxation error, and fit the exponentially decaying curve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:49.335962Z",
     "start_time": "2019-12-10T21:50:46.946840Z"
    }
   },
   "outputs": [],
   "source": [
    "rb_opts2 = rb_opts.copy()\n",
    "rb_opts2['rb_pattern'] = [[0,1]]\n",
    "rb_opts2['length_multiplier'] = 1\n",
    "rb_circs2, xdata2 = rb.randomized_benchmarking_seq(**rb_opts2)\n",
    "\n",
    "noise_model2 = NoiseModel()\n",
    "\n",
    "#Add T1/T2 noise to the simulation\n",
    "t1 = 100.\n",
    "t2 = 80.\n",
    "gate1Q = 0.1\n",
    "gate2Q = 0.5\n",
    "noise_model2.add_all_qubit_quantum_error(thermal_relaxation_error(t1,t2,gate1Q), 'u2')\n",
    "noise_model2.add_all_qubit_quantum_error(thermal_relaxation_error(t1,t2,2*gate1Q), 'u3')\n",
    "noise_model2.add_all_qubit_quantum_error(\n",
    "    thermal_relaxation_error(t1,t2,gate2Q).tensor(thermal_relaxation_error(t1,t2,gate2Q)), 'cx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:51:13.492799Z",
     "start_time": "2019-12-10T21:50:49.337940Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating\n"
     ]
    }
   ],
   "source": [
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 500\n",
    "result_list2 = []\n",
    "transpile_list2 = []\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_circs2):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    rb_circ_transpile = qiskit.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = qiskit.execute(rb_circ_transpile, noise_model=noise_model, shots=shots, backend=backend, backend_options={'max_parallel_experiments': 0})\n",
    "    result_list2.append(job.result())\n",
    "    transpile_list2.append(rb_circ_transpile)    \n",
    "print(\"Finished Simulating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:51:13.840201Z",
     "start_time": "2019-12-10T21:51:13.497713Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create an RBFitter object \n",
    "rbfit = rb.RBFitter(result_list2, xdata2, rb_opts2['rb_pattern'])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.gca()\n",
    "\n",
    "# Plot the essence by calling plot_rb_data\n",
    "rbfit.plot_rb_data(0, ax=ax, add_label=True, show_plt=False)\n",
    "\n",
    "# Add title and label\n",
    "ax.set_title('2 Qubit RB with T1/T2 noise', fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We count again the number of **gates per Clifford** as before, and calculate the **two-qubit Clifford gate error**, using the predicted primitive gate errors from the coherence limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:51:13.929633Z",
     "start_time": "2019-12-10T21:51:13.843050Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of u1 gates per Clifford: 0.250761 \n",
      "Number of u2 gates per Clifford: 0.969565 \n",
      "Number of u3 gates per Clifford: 0.489130 \n",
      "Number of cx gates per Clifford: 1.503696 \n"
     ]
    }
   ],
   "source": [
    "#Count the number of single and 2Q gates in the 2Q Cliffords\n",
    "gates_per_cliff = rb.rb_utils.gates_per_clifford(transpile_list2,xdata[0],basis_gates,rb_opts2['rb_pattern'][0])\n",
    "for basis_gate in basis_gates:\n",
    "    print(\"Number of %s gates per Clifford: %f \"%(basis_gate ,\n",
    "                                                  np.mean([gates_per_cliff[0][basis_gate],\n",
    "                                                           gates_per_cliff[1][basis_gate]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted 2Q Error per Clifford: 1.313111e-02\n"
     ]
    }
   ],
   "source": [
    "# Predicted primitive gate errors from the coherence limit\n",
    "u2_error = rb.rb_utils.coherence_limit(1,[t1],[t2],gate1Q)\n",
    "u3_error = rb.rb_utils.coherence_limit(1,[t1],[t2],2*gate1Q)\n",
    "epg_2q = rb.rb_utils.coherence_limit(2,[t1,t1],[t2,t2],gate2Q)\n",
    "epgs_1q = {'u1': 0, 'u2': u2_error, 'u3': u3_error}\n",
    "pred_epc = rb.rb_utils.calculate_2q_epc(\n",
    "    gate_per_cliff=gates_per_cliff,\n",
    "    epg_2q=epg_2q,\n",
    "    qubit_pair=[0, 1],\n",
    "    list_epgs_1q=[epgs_1q, epgs_1q])\n",
    "\n",
    "# Calculate the predicted epc\n",
    "print(\"Predicted 2Q Error per Clifford: %e\"%pred_epc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:51:14.059488Z",
     "start_time": "2019-12-10T21:51:13.949841Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.16.1</td></tr><tr><td>Terra</td><td>0.13.0.dev0+98fd14e</td></tr><tr><td>Aer</td><td>0.4.1</td></tr><tr><td>Ignis</td><td>0.3.0.dev0+dd2c926</td></tr><tr><td>Aqua</td><td>0.6.4</td></tr><tr><td>IBM Q Provider</td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.6 (default, Jan  8 2020, 19:59:22) \n",
       "[GCC 7.3.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>62.920127868652344</td></tr><tr><td colspan='2'>Fri Mar 27 15:44:43 2020 IST</td></tr></table>"
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       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2020.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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